Hyperbolic geometry offers a natural focus + context for data visualization and has been shown to underlie real-world complex networks. However, current hyperbolic network visualization approaches are limited to special types of networks and do not scale to large datasets. With this in mind, we designed, implemented, and analyzed three methods for hyperbolic visualization of networks in the browser based on inverse projections, generalized force-directed algorithms, and hyperbolic multi-dimensional scaling (H-MDS). A comparison with Euclidean MDS shows that H-MDS produces embeddings with lower distortion for several types of networks. All three methods can handle node-link representations and are available in fully functional web-based systems.

翻译：超曲几何为数据可视化提供了自然焦点+背景,并被显示为真实世界复杂网络的基础。然而,目前的双曲网络可视化方法仅限于特殊类型的网络,不至于大规模数据集。考虑到这一点,我们设计、实施和分析了三种基于反向预测、通用武力引导算法和双曲多维缩放的浏览器中网络双曲直观化方法。与欧洲超clidean MDS的比较表明,H-MDS为几种网络的嵌入,其扭曲程度较低。所有三种方法都能够处理节点链接的表示方式,并可在功能齐全的网络系统中使用。